For bounded effect sizes
data_effectsizes |>
filter(type %in% c("peta2", "r", "abs_r", "rho", "abs_rho", "R2", "OR")) |>
group_by(type) |>
summarize(n = n(),
percent_impossible = round_half_up(mean(!possible)*100, 3),
n_impossible = sum(!possible)) |>
arrange(desc(percent_impossible)) |>
kable() |>
kable_classic(full_width = FALSE)| type | n | percent_impossible | n_impossible |
|---|---|---|---|
| R2 | 8160 | 5.723 | 467 |
| abs_r | 131669 | 0.774 | 1019 |
| r | 131669 | 0.774 | 1019 |
| abs_rho | 3149 | 0.445 | 14 |
| rho | 3149 | 0.445 | 14 |
| OR | 43890 | 0.011 | 5 |
| peta2 | 1219322 | 0.002 | 29 |
data_effectsizes_possible |>
distinct(doi, .keep_all = TRUE) |>
count() |>
kable() |>
kable_classic(full_width = FALSE)| n |
|---|
| 173926 |
data_effectsizes_possible |>
distinct(doi, .keep_all = TRUE) |>
count(subfield) |>
kable() |>
kable_classic(full_width = FALSE)| subfield | n |
|---|---|
| Applied Psychology | 20262 |
| Clinical Psychology | 28944 |
| Developmental and Educational Psychology | 51949 |
| Experimental and Cognitive Psychology | 12662 |
| General Psychology | 46084 |
| Social Psychology | 14025 |
# data_effectsizes_possible |>
# distinct(doi, .keep_all = TRUE) |>
# count(journal) |>
# kable() |>
# kable_classic(full_width = FALSE)
data_effectsizes_possible |>
distinct(journal) |>
count() |>
kable() |>
kable_classic(full_width = FALSE)| n |
|---|
| 386 |
data_effectsizes_possible |>
distinct(year) |>
arrange(desc(year)) |>
slice(1, n()) |>
kable() |>
kable_classic(full_width = FALSE)| year |
|---|
| 2024 |
| 2004 |
data_effectsizes_possible |>
count(type) |>
arrange(desc(n)) |>
kable() |>
kable_classic(full_width = FALSE)| type | n |
|---|---|
| peta2 | 1219293 |
| d_s | 454687 |
| d_z | 454687 |
| chi2 | 185020 |
| abs_stdB | 143581 |
| stdB | 143581 |
| abs_r | 130650 |
| r | 130650 |
| B | 104679 |
| OR | 43885 |
| logOR | 43885 |
| abs_OR | 43868 |
| d_native | 23089 |
| sqrtR2 | 8150 |
| R2 | 7693 |
| Wald | 5941 |
| abs_rho | 3135 |
| rho | 3135 |
8 largest Cohen’s d values in the database, by unique DOI.
data_effectsizes_possible |>
filter(type %in% "d_native") |> # c("d_native", "abs_r", "R2")) |>
group_by(type) |>
arrange(desc(estimate)) |>
slice_head(n = 14) |>
select(doi, type, estimate) |>
distinct(doi, .keep_all = TRUE) |>
kable() |>
kable_classic(full_width = FALSE)| doi | type | estimate |
|---|---|---|
| https://doi.org/10.1016/j.jvb.2010.04.006 | d_native | 919.17 |
| https://doi.org/10.1007/s10461-007-9346-0 | d_native | 690.00 |
| https://doi.org/10.1177/1073191113517929 | d_native | 535.25 |
| https://doi.org/10.3389/fpsyg.2019.02293 | d_native | 339.45 |
| https://doi.org/10.1016/j.mhpa.2017.04.004 | d_native | 204.00 |
| https://doi.org/10.3389/fpsyg.2021.655168 | d_native | 181.30 |
| https://doi.org/10.1016/j.appet.2014.03.030 | d_native | 146.00 |
| https://doi.org/10.3389/fpsyg.2015.00520 | d_native | 117.22 |
Purvanova and Muros (2010)
https://doi.org/10.1016/j.jvb.2010.04.006
“Q_D = 919.17” (p. 174)
Q statistic incorrectly extracted as d.
[no pubpeer comment needed]
Atkinson et al., (2010)
Multiple Sexual Partnerships in a Sample of African-American Crack Smokers, https://doi.org/10.1007/s10461-007-9346-0
“Women had had significantly more total sex partners in the 30 days preceding their interview, on average, than men (14.2 vs. 7.9, t =-4.48, d = 690, P < .01).” (p. 51)
d was indeed reported, but does not reproduce from the t test:
SDs implied by differences in mean:
Implied \(SD_{pooled}\) = 0.0091304. This would require that almost all of the 497 males and 195 female in the sample each reported the same number of sexual partners, within each group.
Recalculated d from t value and df:
effectsize::t_to_d(t = 4.48,
df_error = 497 + 195 - 2,
paired = FALSE,
ci = 0.95,
alternative = "two.sided")## d | 95% CI
## -------------------
## 0.34 | [0.19, 0.49]
Given that recalculated df from the Ns (497 + 195 - 2) = 609 matches the reported d, I suspect that the t-test’s df has been mislabeled as the effect size.
https://pubpeer.com/publications/DDDBEA2AB1381B343EF23E2028F75B#1
Eddington et al. (2014)
https://doi.org/10.1177/1073191113517929
“Mardia’s test (Mardia, 1970) indicated significant departures from multivariate kurtosis (b2,d = 535.25, p < .001)” (p. 7)
Other test statistic incorrectly extracted as d.
[no pubpeer comment needed]
Hoyo et al. (2019)
https://doi.org/10.3389/fpsyg.2019.02293
on the “A new version of the Dots spatial conflict task” used to measure “Executive Function” in children, “Participants took more time to respond to incongruent than congruent trials (d = 132.44, p < 0.001), and in the mixed block compared to the simple blocks (d = 339.45, p < 0.001).” (p. 8)
d was indeed reported, but does not seem to reproduce from the reported Ms and SDs (.xlsx in zotero); very roughly recalculated d for the simple vs mixed comparison d = 1.4
https://pubpeer.com/publications/A983972167F472FFCC0AFAEE921216#
Firth et al. (2017)
[paraphrasing] “Reductions in general symptoms after a 10 week exercise program in psychiatric inpatients”: Cohen’s d = 204.
https://doi.org/10.1016/j.mhpa.2017.04.004
This is likely to be simply a typo: adding a decimal place to make the effect size d = .204 would bring it in line with other effect sizes reported in the paragraph.
https://pubpeer.com/publications/24998D2B750BA7927040318CCBD6C9#
Bogliotti & Isel (2021)
[paraphrasing] “differences in reaction times between experimental conditions in a behavioral paradigm”: d = 181 and 54.
https://doi.org/10.3389/fpsyg.2021.655168
SDs likely misreported as Cohen’s ds.
https://pubpeer.com/publications/FEC504747ABE62EE2071B3EF4A2ED7#1
van der Horst et al. (2014)
[paraphrasing] “when children do the cooking they consume more calories than when parents do the cooking”: “(d =146 kcal; 47%; P = .004)” (p. 22)
http://dx.doi.org/10.1016/j.appet.2014.03.030
d seems to refer to delta, the difference in means between conditions, rather than Cohen’s d - although this is not explicated anywhere.
[no pubpeer comment needed]
Bennett et al. (2015)
ANOVA applied to RT data: “Pairwise comparisons indicated that the mean response latency during the first block were significantly longer than those in the third block, d = 95.11, SE = 30.91, p = 0.02, and fourth block, d = 117.22, SE = 36.10, p = 0.01.” (p. 8)
https://doi.org/10.3389/fpsyg.2015.00520
d seems to refer to delta, the difference in mean RTs between conditions, rather than Cohen’s d - although this is not explicated anywhere.
[no pubpeer comment needed]
data_percentiles_long <- data_effectsizes_possible |>
filter(type %in% unique(type)) |>
group_by(type) |>
summarise(
across(
.cols = everything(),
.fns = list,
.names = "{.col}_list"
), # just for clarity — we only care about estimate column
.groups = "drop_last"
) |>
select(type, estimate = estimate_list) |>
unnest(estimate) |>
group_by(type) |>
summarise(
percentile = c(1, 5, 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 95, 99) / 100,
value = map_dbl(percentile, ~ quantile(estimate, probs = .x, na.rm = TRUE)),
.groups = "drop"
) |>
mutate(percentile = percentile * 100)
data_percentiles <- data_percentiles_long |>
pivot_wider(names_from = type, values_from = value) |>
select(percentile,
d_native,
d_s,
d_z,
abs_r,
#r,
#rho,
#abs_rho,
#sqrtR2,
peta2,
R2,
#OR,
abs_OR,
#logOR,
chi2,
B,
#abs_B,
stdB,
abs_stdB)
#Walddata_percentiles |>
mutate_if(is.numeric, round_half_up, digits = 2) |>
kable() |>
kable_classic(full_width = FALSE)| percentile | d_native | d_s | d_z | abs_r | peta2 | R2 | abs_OR | chi2 | B | stdB | abs_stdB |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.02 | 0.02 | 0.01 | 0.01 | 0.00 | 0.00 | 1.01 | 0.03 | -1.36 | -0.42 | 0.00 |
| 5 | 0.07 | 0.09 | 0.04 | 0.06 | 0.00 | 0.01 | 1.03 | 0.57 | -0.01 | 0.00 | 0.02 |
| 10 | 0.13 | 0.17 | 0.08 | 0.11 | 0.01 | 0.01 | 1.08 | 1.60 | 0.01 | 0.03 | 0.05 |
| 20 | 0.24 | 0.30 | 0.15 | 0.17 | 0.03 | 0.03 | 1.19 | 4.29 | 0.06 | 0.08 | 0.10 |
| 25 | 0.29 | 0.36 | 0.18 | 0.20 | 0.04 | 0.04 | 1.27 | 5.40 | 0.08 | 0.10 | 0.12 |
| 30 | 0.34 | 0.42 | 0.21 | 0.22 | 0.05 | 0.06 | 1.34 | 6.70 | 0.11 | 0.13 | 0.14 |
| 40 | 0.45 | 0.55 | 0.28 | 0.27 | 0.08 | 0.09 | 1.53 | 10.16 | 0.18 | 0.17 | 0.18 |
| 50 | 0.56 | 0.71 | 0.36 | 0.33 | 0.11 | 0.13 | 1.76 | 15.97 | 0.27 | 0.22 | 0.23 |
| 60 | 0.70 | 0.90 | 0.46 | 0.38 | 0.16 | 0.17 | 2.05 | 27.16 | 0.40 | 0.28 | 0.29 |
| 70 | 0.89 | 1.17 | 0.59 | 0.45 | 0.24 | 0.24 | 2.49 | 53.12 | 0.61 | 0.35 | 0.36 |
| 75 | 1.01 | 1.34 | 0.68 | 0.49 | 0.29 | 0.28 | 2.80 | 79.98 | 0.79 | 0.41 | 0.42 |
| 80 | 1.14 | 1.57 | 0.80 | 0.54 | 0.36 | 0.33 | 3.25 | 129.78 | 1.09 | 0.47 | 0.49 |
| 90 | 1.61 | 2.40 | 1.23 | 0.67 | 0.55 | 0.48 | 5.26 | 441.13 | 2.51 | 0.75 | 0.78 |
| 95 | 2.30 | 3.40 | 1.74 | 0.78 | 0.71 | 0.62 | 8.85 | 986.36 | 5.62 | 1.59 | 1.73 |
| 99 | 7.14 | 7.48 | 3.87 | 0.94 | 0.91 | 0.89 | 33.33 | 4247.75 | 32.24 | 12.41 | 14.65 |
# data_percentiles |>
# pivot_longer(col = -percentile,
# names_to = "estimator",
# values_to = "estimate") |>
# ggplot(aes(as.factor(percentile), estimate)) +
# geom_bar(stat = "identity", width = 0.8) +
# theme_linedraw() +
# facet_wrap(~ estimator, scales = "free")
#
# data_percentiles |>
# pivot_longer(col = -percentile,
# names_to = "estimator",
# values_to = "estimate") |>
# mutate(estimator = fct_relevel(estimator,
# "d_native", "d_s", "d_z",
# "abs_r", "R2", "peta2",
# "abs_OR")) |>
# filter(estimator %in% c("d_native", "d_s", "d_z", "abs_OR", "peta2", "abs_r", "R2")) |>
# ggplot(aes(as.factor(percentile), estimate)) +
# geom_bar(stat = "identity", width = 0.8) +
# theme_linedraw() +
# facet_wrap(~ estimator, scales = "free")data_percentiles_for_plot <- data_percentiles_long |>
filter(type %in% c("d_native",
"d_s",
"d_z",
"abs_r",
"peta2",
"R2",
"abs_OR",
"abs_stdB",
"chi2")) |>
mutate(type_lab = case_when(
type == "d_native" ~ "\"Cohen's\"~'|'*italic(d)*'|'~'(reported)'",
type == "d_s" ~ "\"Cohen's\"~'|'*italic(d)[s]*'|'~'(from t-test)'",
type == "d_z" ~ "\"Cohen's\"~'|'*italic(d)[z]*'|'~'(from t-test)'",
type == "abs_r" ~ "\"Pearson's\"~'|'*italic(r)*'|'",
type == "peta2" ~ "eta[p]^2",
type == "R2" ~ "italic(R)^2",
type == "abs_OR" ~ "\"|Odds Ratio|\"",
type == "abs_stdB" ~ "'|'*italic(beta)*'|'",
type == "chi2" ~ "chi^2",
TRUE ~ type
)) |>
mutate(type_lab = fct_relevel(
type_lab,
"\"Cohen's\"~'|'*italic(d)*'|'~'(reported)'",
"\"Cohen's\"~'|'*italic(d)[s]*'|'~'(from t-test)'",
"\"Cohen's\"~'|'*italic(d)[z]*'|'~'(from t-test)'",
"\"Pearson's\"~'|'*italic(r)*'|'",
"eta[p]^2",
"italic(R)^2",
"\"|Odds Ratio|\"",
"'|'*italic(beta)*'|'",
"chi^2"
)) |>
drop_na() labs_map <- c(
d_native = "\"Cohen's\"~group('|',italic(d),'|')~'(reported)'",
d_s = "\"Independent Cohen's\"~group('|',italic(d)[s],'|')~'(recalculated from t-test)'",
d_z = "\"Dependent Cohen's\"~group('|',italic(d)[z],'|')~'(recalculated from t-test)'"
)
label_map_parsed <- function(map) {
force(map)
function(x) parse(text = unname(map[x]))
}
data_percentiles_for_plot |>
filter(percentile < 99) |>
filter(str_detect(type, "d_")) |>
mutate(type = fct_relevel(type, "d_z", "d_native", "d_s")) |>
ggplot(aes(value, percentile, color = type)) +
geom_line() +
geom_point() +
scale_y_continuous(
#breaks = c(1, seq(5, 95, 5), 99),
breaks = c(1, seq(5, 95, 5)),
name = "Percentile"
) +
scale_x_continuous(
breaks = scales::breaks_pretty(n = 8),
#limits = c(0, 1),
name = expression("Cohen's" ~ group("|", italic(d), "|"))
) +
theme_linedraw() +
theme(
panel.grid.minor = element_blank(),
strip.placement = "outside",
strip.background = element_blank(), # no fill or box
strip.text = element_text(colour = "black"),
legend.position = c(0.65, 0.3)
) +
scale_colour_discrete(
name = "Method",
breaks = c("d_z", "d_native", "d_s"), # desired legend order
#breaks = names(labs_map),
labels = label_map_parsed(labs_map)
)data_percentiles_for_plot |>
filter(percentile < 99) |>
filter(!str_detect(type, "d_")) |>
ggplot(aes(value, percentile)) +
geom_line() +
geom_point() +
scale_y_continuous(
#breaks = c(1, seq(5, 95, 5), 99),
breaks = c(1, seq(5, 95, 5)),
name = "Percentile"
) +
scale_x_continuous(
breaks = scales::breaks_pretty(n = 8),
#limits = c(0, 1),
name = "Effect size"
) +
theme_linedraw() +
theme(
panel.grid.minor = element_blank(),
strip.placement = "outside",
strip.background = element_blank(), # no fill or box
strip.text = element_text(colour = "black")
#legend.position = c(0.8, 0.4)
) +
facet_wrap(~ type_lab,
scales = "free",
strip.position = "bottom",
labeller = label_parsed)# data_percentiles_by_subfield <-
# data_effectsizes_possible |>
# filter(type %in% unique(type)) |>
# group_by(type, subfield) |>
# summarise(
# across(
# .cols = everything(),
# .fns = list,
# .names = "{.col}_list"
# ), # just for clarity — we only care about estimate column
# .groups = "drop_last"
# ) |>
# select(type, subfield, estimate = estimate_list) |>
# unnest(estimate) |>
# group_by(type, subfield) |>
# summarise(
# percentile = c(1, 5, 10, 25, 50, 75, 90, 95, 99) / 100,
# value = map_dbl(percentile, ~ quantile(estimate, probs = .x, na.rm = TRUE)),
# .groups = "drop"
# ) |>
# mutate(percentile = percentile * 100) |>
# pivot_wider(names_from = type, values_from = value) |>
# select(subfield,
# percentile,
# d_native,
# d_s,
# d_z,
#
# abs_r,
# #r,
# #rho,
# #abs_rho,
# #sqrtR2,
#
# peta2,
# R2,
#
# #OR,
# abs_OR,
# #logOR,
#
# chi2,
#
# B,
# #abs_B,
# stdB,
# abs_stdB) |>
# #Wald
# arrange(subfield, percentile)
#
# data_percentiles_by_subfield |>
# mutate_if(is.numeric, round_half_up, digits = 2) |>
# kable() |>
# kable_classic(full_width = FALSE)
# data_percentiles_by_subfield |>
# pivot_longer(col = -c(percentile, subfield),
# names_to = "estimator",
# values_to = "estimate") |>
# ggplot(aes(as.factor(percentile), estimate)) +
# geom_bar(stat = "identity", width = 0.8) +
# theme_linedraw() +
# facet_grid(subfield ~ estimator, scales = "free")
# data_percentiles_by_subfield |>
# pivot_longer(col = -c(percentile, subfield),
# names_to = "estimator",
# values_to = "estimate") |>
# mutate(estimator = fct_relevel(estimator,
# "d_native", "d_s", "d_z",
# "abs_r", "R2", "peta2",
# "abs_OR")) |>
# filter(estimator %in% c("d_native", "d_s", "d_z", "abs_OR", "peta2", "abs_r", "R2")) |>
# ggplot(aes(as.factor(percentile), estimate)) +
# geom_bar(stat = "identity", width = 0.8) +
# theme_linedraw() +
# facet_grid(estimator ~ subfield, scales = "free")
#
# data_percentiles_by_subfield |>
# pivot_longer(col = -c(percentile, subfield),
# names_to = "estimator",
# values_to = "estimate") |>
# mutate(estimator = fct_relevel(estimator,
# "d_native", "d_s", "d_z",
# "abs_r", "R2", "peta2",
# "abs_OR")) |>
# filter(estimator %in% c("d_native", "d_s", "d_z", "abs_OR", "peta2", "abs_r", "R2")) |>
# ggplot(aes(as.factor(percentile), estimate, fill = subfield)) +
# geom_bar(stat = "identity", width = 0.8, position = position_dodge(width = .8), color = "black") +
# theme_linedraw() +
# facet_wrap( ~ estimator, scales = "free")data_percentiles_by_subfield <-
data_effectsizes_possible |>
filter(type %in% unique(type)) |>
group_by(type, subfield) |>
summarise(
across(
.cols = everything(),
.fns = list,
.names = "{.col}_list"
), # just for clarity — we only care about estimate column
.groups = "drop_last"
) |>
select(type, subfield, estimate = estimate_list) |>
unnest(estimate) |>
group_by(type, subfield) |>
summarise(
percentile = c(1, 5, 10, 25, 50, 75, 90, 95, 99) / 100,
value = map_dbl(percentile, ~ quantile(estimate, probs = .x, na.rm = TRUE)),
.groups = "drop"
) |>
mutate(percentile = percentile * 100) |>
pivot_wider(names_from = subfield, values_from = value) |>
filter(type %in% c("d_native",
"d_s",
"d_z",
"abs_r",
"peta2",
"R2",
"abs_OR",
"chi2",
"B",
"stdB",
"abs_stdB")) |>
mutate(type = fct_relevel(type,
"d_native",
"d_s",
"d_z",
"abs_r",
"peta2",
"R2",
"abs_OR",
"chi2",
"B",
"stdB",
"abs_stdB")) |>
arrange(type, percentile)
data_percentiles_by_subfield |>
mutate_if(is.numeric, round_half_up, digits = 2) |>
kable() |>
kable_classic(full_width = FALSE)| type | percentile | Applied Psychology | Clinical Psychology | Developmental and Educational Psychology | Experimental and Cognitive Psychology | General Psychology | Social Psychology |
|---|---|---|---|---|---|---|---|
| d_native | 1 | 0.01 | 0.01 | 0.02 | 0.02 | 0.01 | 0.01 |
| d_native | 5 | 0.07 | 0.08 | 0.08 | 0.09 | 0.08 | 0.05 |
| d_native | 10 | 0.12 | 0.13 | 0.14 | 0.16 | 0.14 | 0.10 |
| d_native | 25 | 0.26 | 0.30 | 0.30 | 0.35 | 0.29 | 0.23 |
| d_native | 50 | 0.49 | 0.56 | 0.57 | 0.66 | 0.56 | 0.45 |
| d_native | 75 | 0.86 | 0.99 | 1.02 | 1.15 | 1.03 | 0.87 |
| d_native | 90 | 1.34 | 1.47 | 1.57 | 1.83 | 1.81 | 1.45 |
| d_native | 95 | 1.78 | 2.05 | 2.20 | 2.55 | 2.97 | 1.96 |
| d_native | 99 | 4.74 | 6.13 | 7.18 | 6.87 | 13.06 | 3.97 |
| d_s | 1 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 |
| d_s | 5 | 0.08 | 0.08 | 0.09 | 0.10 | 0.08 | 0.07 |
| d_s | 10 | 0.15 | 0.16 | 0.18 | 0.20 | 0.15 | 0.14 |
| d_s | 25 | 0.31 | 0.33 | 0.40 | 0.47 | 0.33 | 0.30 |
| d_s | 50 | 0.55 | 0.63 | 0.80 | 0.92 | 0.65 | 0.53 |
| d_s | 75 | 1.02 | 1.16 | 1.46 | 1.68 | 1.26 | 0.94 |
| d_s | 90 | 1.86 | 2.06 | 2.55 | 2.91 | 2.27 | 1.73 |
| d_s | 95 | 2.68 | 2.86 | 3.60 | 4.08 | 3.24 | 2.51 |
| d_s | 99 | 5.49 | 5.73 | 8.37 | 9.54 | 7.05 | 5.09 |
| d_z | 1 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 |
| d_z | 5 | 0.04 | 0.04 | 0.05 | 0.05 | 0.04 | 0.04 |
| d_z | 10 | 0.08 | 0.08 | 0.09 | 0.10 | 0.07 | 0.07 |
| d_z | 25 | 0.16 | 0.17 | 0.20 | 0.24 | 0.17 | 0.15 |
| d_z | 50 | 0.28 | 0.32 | 0.41 | 0.47 | 0.33 | 0.27 |
| d_z | 75 | 0.52 | 0.59 | 0.75 | 0.86 | 0.64 | 0.48 |
| d_z | 90 | 0.94 | 1.05 | 1.31 | 1.50 | 1.16 | 0.87 |
| d_z | 95 | 1.36 | 1.46 | 1.84 | 2.10 | 1.66 | 1.27 |
| d_z | 99 | 2.83 | 2.95 | 4.36 | 4.95 | 3.60 | 2.59 |
| abs_r | 1 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 |
| abs_r | 5 | 0.06 | 0.06 | 0.06 | 0.05 | 0.06 | 0.05 |
| abs_r | 10 | 0.11 | 0.11 | 0.11 | 0.09 | 0.11 | 0.09 |
| abs_r | 25 | 0.19 | 0.21 | 0.20 | 0.20 | 0.19 | 0.17 |
| abs_r | 50 | 0.31 | 0.33 | 0.33 | 0.35 | 0.32 | 0.30 |
| abs_r | 75 | 0.48 | 0.50 | 0.50 | 0.53 | 0.49 | 0.46 |
| abs_r | 90 | 0.66 | 0.67 | 0.67 | 0.74 | 0.66 | 0.63 |
| abs_r | 95 | 0.77 | 0.77 | 0.79 | 0.85 | 0.76 | 0.74 |
| abs_r | 99 | 0.93 | 0.92 | 0.93 | 0.97 | 0.93 | 0.92 |
| peta2 | 1 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| peta2 | 5 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.00 |
| peta2 | 10 | 0.01 | 0.01 | 0.01 | 0.02 | 0.01 | 0.01 |
| peta2 | 25 | 0.02 | 0.04 | 0.04 | 0.06 | 0.03 | 0.02 |
| peta2 | 50 | 0.07 | 0.11 | 0.13 | 0.16 | 0.10 | 0.07 |
| peta2 | 75 | 0.20 | 0.26 | 0.31 | 0.39 | 0.27 | 0.18 |
| peta2 | 90 | 0.43 | 0.50 | 0.57 | 0.65 | 0.53 | 0.38 |
| peta2 | 95 | 0.61 | 0.65 | 0.72 | 0.79 | 0.70 | 0.55 |
| peta2 | 99 | 0.87 | 0.89 | 0.91 | 0.93 | 0.91 | 0.81 |
| R2 | 1 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| R2 | 5 | 0.00 | 0.00 | 0.01 | 0.01 | 0.01 | 0.01 |
| R2 | 10 | 0.01 | 0.01 | 0.01 | 0.02 | 0.01 | 0.01 |
| R2 | 25 | 0.03 | 0.05 | 0.05 | 0.08 | 0.04 | 0.04 |
| R2 | 50 | 0.11 | 0.13 | 0.12 | 0.20 | 0.12 | 0.12 |
| R2 | 75 | 0.25 | 0.30 | 0.27 | 0.39 | 0.26 | 0.27 |
| R2 | 90 | 0.48 | 0.51 | 0.46 | 0.62 | 0.45 | 0.43 |
| R2 | 95 | 0.64 | 0.64 | 0.59 | 0.75 | 0.59 | 0.52 |
| R2 | 99 | 0.95 | 0.80 | 0.86 | 0.91 | 0.91 | 0.73 |
| abs_OR | 1 | 1.00 | 1.00 | 1.01 | 1.01 | 1.01 | 1.00 |
| abs_OR | 5 | 1.03 | 1.03 | 1.04 | 1.03 | 1.04 | 1.03 |
| abs_OR | 10 | 1.06 | 1.06 | 1.10 | 1.07 | 1.08 | 1.08 |
| abs_OR | 25 | 1.25 | 1.23 | 1.30 | 1.23 | 1.26 | 1.30 |
| abs_OR | 50 | 1.71 | 1.71 | 1.82 | 1.79 | 1.72 | 1.79 |
| abs_OR | 75 | 2.61 | 2.71 | 2.91 | 3.36 | 2.70 | 2.80 |
| abs_OR | 90 | 4.64 | 4.98 | 5.56 | 7.27 | 5.00 | 5.18 |
| abs_OR | 95 | 7.08 | 7.94 | 9.43 | 14.79 | 8.86 | 8.40 |
| abs_OR | 99 | 23.66 | 24.75 | 39.32 | 72.74 | 33.33 | 23.47 |
| chi2 | 1 | 0.06 | 0.03 | 0.03 | 0.01 | 0.04 | 0.05 |
| chi2 | 5 | 1.00 | 0.51 | 0.55 | 0.24 | 0.63 | 1.00 |
| chi2 | 10 | 2.28 | 1.42 | 1.59 | 0.80 | 1.88 | 2.24 |
| chi2 | 25 | 6.40 | 4.90 | 5.32 | 3.79 | 6.07 | 6.03 |
| chi2 | 50 | 23.40 | 13.33 | 14.99 | 10.67 | 19.20 | 18.18 |
| chi2 | 75 | 175.85 | 62.48 | 64.27 | 37.84 | 118.93 | 90.40 |
| chi2 | 90 | 761.99 | 348.78 | 321.33 | 168.85 | 636.18 | 450.36 |
| chi2 | 95 | 1571.09 | 866.00 | 768.67 | 449.24 | 1387.13 | 946.78 |
| chi2 | 99 | 5286.28 | 4386.75 | 3533.51 | 2569.91 | 5867.32 | 3723.77 |
| B | 1 | -0.62 | -4.82 | -0.82 | -3.82 | -1.97 | -0.42 |
| B | 5 | 0.00 | -0.09 | 0.00 | -0.04 | -0.13 | 0.00 |
| B | 10 | 0.02 | 0.01 | 0.02 | 0.01 | 0.01 | 0.02 |
| B | 25 | 0.10 | 0.06 | 0.09 | 0.08 | 0.08 | 0.09 |
| B | 50 | 0.29 | 0.28 | 0.28 | 0.35 | 0.24 | 0.26 |
| B | 75 | 0.74 | 0.99 | 0.97 | 1.25 | 0.60 | 0.74 |
| B | 90 | 2.11 | 3.37 | 2.88 | 4.16 | 1.81 | 2.15 |
| B | 95 | 4.92 | 7.48 | 6.18 | 9.89 | 4.30 | 4.48 |
| B | 99 | 39.71 | 30.81 | 28.42 | 73.16 | 38.14 | 19.65 |
| stdB | 1 | -0.21 | -0.53 | -0.37 | -1.98 | -0.44 | -0.28 |
| stdB | 5 | 0.01 | 0.00 | 0.00 | 0.00 | -0.09 | 0.01 |
| stdB | 10 | 0.04 | 0.02 | 0.03 | 0.02 | 0.02 | 0.03 |
| stdB | 25 | 0.12 | 0.09 | 0.10 | 0.11 | 0.11 | 0.10 |
| stdB | 50 | 0.24 | 0.22 | 0.21 | 0.26 | 0.22 | 0.20 |
| stdB | 75 | 0.45 | 0.42 | 0.39 | 0.58 | 0.39 | 0.38 |
| stdB | 90 | 0.81 | 1.00 | 0.79 | 1.89 | 0.62 | 0.71 |
| stdB | 95 | 1.77 | 2.34 | 1.74 | 4.63 | 0.87 | 1.42 |
| stdB | 99 | 15.04 | 13.68 | 12.11 | 47.85 | 6.40 | 9.05 |
| abs_stdB | 1 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| abs_stdB | 5 | 0.02 | 0.02 | 0.02 | 0.02 | 0.03 | 0.02 |
| abs_stdB | 10 | 0.05 | 0.04 | 0.05 | 0.04 | 0.06 | 0.05 |
| abs_stdB | 25 | 0.13 | 0.11 | 0.11 | 0.13 | 0.12 | 0.11 |
| abs_stdB | 50 | 0.24 | 0.23 | 0.22 | 0.28 | 0.23 | 0.21 |
| abs_stdB | 75 | 0.45 | 0.44 | 0.40 | 0.63 | 0.40 | 0.39 |
| abs_stdB | 90 | 0.83 | 1.08 | 0.83 | 2.11 | 0.64 | 0.73 |
| abs_stdB | 95 | 1.80 | 2.59 | 1.91 | 5.80 | 0.90 | 1.48 |
| abs_stdB | 99 | 15.81 | 15.13 | 14.36 | 49.40 | 7.16 | 9.44 |
## # A tibble: 18 Ă— 2
## type n
## <chr> <int>
## 1 B 104679
## 2 OR 43885
## 3 R2 7693
## 4 Wald 5941
## 5 abs_OR 43868
## 6 abs_r 130650
## 7 abs_rho 3135
## 8 abs_stdB 143581
## 9 chi2 185020
## 10 d_native 23089
## 11 d_s 454687
## 12 d_z 454687
## 13 logOR 43885
## 14 peta2 1219293
## 15 r 130650
## 16 rho 3135
## 17 sqrtR2 8150
## 18 stdB 143581
data_effectsizes_possible |>
filter(type == "d_native") |>
group_by(type) |>
filter(estimate <= quantile(estimate, .99, na.rm = TRUE)) |>
ggplot(aes(x = estimate, y = subfield, fill = subfield)) +
geom_density_ridges(alpha = 1) +
theme_linedraw() +
scale_x_continuous(breaks = breaks_pretty(n = 8)) +
labs(x = "Cohen's d native",
y = "") +
scale_fill_viridis_d() +
theme(legend.position = "none")data_effectsizes_possible |>
filter(type == "d_s") |>
group_by(type) |>
filter(estimate <= quantile(estimate, .99, na.rm = TRUE)) |>
ggplot(aes(x = estimate, y = subfield, fill = subfield)) +
geom_density_ridges(alpha = 1) +
theme_linedraw() +
scale_x_continuous(breaks = breaks_pretty(n = 8)) +
labs(x = "Cohen's d_s",
y = "") +
scale_fill_viridis_d() +
theme(legend.position = "none")data_effectsizes_possible |>
filter(type == "d_z") |>
group_by(type) |>
filter(estimate <= quantile(estimate, .99, na.rm = TRUE)) |>
ggplot(aes(x = estimate, y = subfield, fill = subfield)) +
geom_density_ridges(alpha = 1) +
theme_linedraw() +
scale_x_continuous(breaks = breaks_pretty(n = 8)) +
labs(x = "Cohen's d_s",
y = "") +
scale_fill_viridis_d() +
theme(legend.position = "none")IS THIS TRIMMING THINGS WEIRDLY? WHERE IS THE ABSOLUTE SCORING IN THE BELOW?
plot_es <- function(data, x, trim_lower = 0, trim_upper = 1, binwidth = 0.1, xlab, title, subtitle) {
data_subset <- data |>
filter(type == x)
# plot limits
p1 <- quantile(data_subset$estimate, trim_lower, na.rm = TRUE)
p99 <- quantile(data_subset$estimate, trim_upper, na.rm = TRUE)
ggplot(data_subset, aes(x = estimate)) +
geom_histogram(binwidth = binwidth, boundary = 0) +
theme_linedraw() +
scale_x_continuous(limits = c(p1, p99), breaks = scales::breaks_pretty(n = 10)) +
scale_y_continuous(breaks = scales::breaks_pretty(n = 6)) +
labs(title = title,
subtitle = subtitle,
x = xlab,
y = "Count")
}
p_d_native <-
plot_es(data_effectsizes_possible, "d_native", trim_upper = 0.99,
xlab = expression("Cohen's "*italic(d)),
title = expression("Absolute Cohen's "*italic(d)),
subtitle = "0–99th percentile range")
p_d_s <-
plot_es(data_effectsizes_possible, "d_s", trim_upper = 0.99,
xlab = expression("Cohen's "*italic(d)[s]),
title = expression("Absolute Cohen's "*italic(d)[s]*" estimated from "*italic(t)*"-test"),
subtitle = "0–99th percentile range")
p_d_z <-
plot_es(data_effectsizes_possible, "d_z", trim_upper = 0.99,
xlab = expression("Cohen's "*italic(d)[z]),
title = expression("Absolute Cohen's "*italic(d)[z]*" estimated from "*italic(t)*"-test"),
subtitle = "0–99th percentile range")
p_peta2 <-
plot_es(data_effectsizes_possible, "peta2", trim_lower = 0.01, trim_upper = 0.99, binwidth = 0.01,
xlab = expression(italic(eta)[p]^2),
title = expression(italic(eta)[p]^2*" estimated from "*italic(F)*"-test"),
subtitle = "1–99th percentile range")
p_abs_r <-
plot_es(data_effectsizes_possible, "abs_r", binwidth = 0.1,
xlab = expression("Pearson's "*italic(r)),
title = expression("Absolute Pearson's "*italic(r)),
subtitle = "0–100th percentile range") +
coord_cartesian(xlim = c(-1, 1))
# data_effectsizes_possible |>
# mutate(r_out_of_bounds = case_when(r < -1 ~ TRUE,
# r > 1 ~ TRUE,
# TRUE ~ FALSE)) |>
# summarize(percent_r_out_of_bounds = mean(r_out_of_bounds)*100)
p_abs_rho <-
plot_es(data_effectsizes_possible, "rho", binwidth = 0.1,
xlab = expression(italic(rho)),
title = expression("Absolute "*italic(rho)),
subtitle = "0–100th percentile range") +
coord_cartesian(xlim = c(-1, 1))
p_sqrtR2 <-
plot_es(data_effectsizes_possible, "sqrtR2", binwidth = 0.1,
xlab = expression("sqrt R"^2),
title = expression("sqrt R"^2),
subtitle = "0–100th percentile range") +
coord_cartesian(xlim = c(-1, 1))
p_R2 <-
plot_es(data_effectsizes_possible, "R2", binwidth = 0.1,
xlab = expression("R"^2),
title = expression("R"^2),
subtitle = "0–100th percentile range") +
coord_cartesian(xlim = c(-1, 1))
# dat_es |>
# mutate(R2_out_of_bounds = case_when(R2 < -1 ~ TRUE,
# R2 > 1 ~ TRUE,
# TRUE ~ FALSE)) |>
# summarize(percent_R2_out_of_bounds = mean(R2_out_of_bounds)*100)
p_stdB <-
plot_es(data_effectsizes_possible, "stdB", trim_lower = 0.01, trim_upper = 0.95, binwidth = 0.01,
xlab = expression(beta),
title = expression(beta),
subtitle = "1–95th percentile range")
p_B <-
plot_es(data_effectsizes_possible, "B", trim_lower = 0.01, trim_upper = 0.95, binwidth = 0.1,
xlab = expression("B"),
title = expression("B"),
subtitle = "1–95th percentile range")
p_wald <-
plot_es(data_effectsizes_possible, "Wald", trim_lower = 0, trim_upper = 0.95, binwidth = 1,
xlab = expression("Wald"),
title = expression("Wald"),
subtitle = "1–95th percentile range")
p_chi2 <-
plot_es(data_effectsizes_possible, "chi2", trim_lower = 0, trim_upper = 0.9, binwidth = 5,
xlab = expression(chi^2),
title = expression(chi^2),
subtitle = "0–90th percentile range of positive values") +
coord_cartesian(xlim = c(0, NA))
p_OR <-
plot_es(data_effectsizes_possible, "OR", trim_lower = 0.01, trim_upper = 0.98,
xlab = expression("OR"),
title = expression("OR"),
subtitle = "1–98th percentile range")
p_abs_OR <-
plot_es(data_effectsizes_possible, "abs_OR", trim_upper = 0.98,
xlab = expression("Absolute OR"),
title = expression("Absolute OR"),
subtitle = "0–98th percentile range")
p_logOR <-
plot_es(data_effectsizes_possible, "logOR", trim_lower = 0.01, trim_upper = 0.99,
xlab = expression("log-odds"),
title = expression("log-odds"),
subtitle = "1–99th percentile range")
# plot_es(data_effectsizes_possible, z, trim_lower = 0.01, trim_upper = 0.98,
# xlab = expression("z-score"),
# title = expression("z-score"),
# subtitle = "1–98th percentile range") +
# geom_vline(xintercept = 1.96, color = "pink", linetype = "dashed")
#
# plot_es(dat_p, p_val, trim_lower = 0, trim_upper = 1, binwidth = 0.001,
# xlab = expression(italic(p)*" value"),
# title = expression(italic(p)*" value"),
# subtitle = "Between 0 and .1") +
# coord_cartesian(xlim = c(0, 0.1))
#
# plot_es(dat_p, p_implied, trim_lower = 0, trim_upper = 1, binwidth = 0.001,
# xlab = expression("Implied "*italic(p)*" value"),
# title = expression("Implied "*italic(p)*" value"),
# subtitle = "Between 0 and .1") +
# coord_cartesian(xlim = c(0, 0.1))quantile_regression_and_plot <- function(data, es_type, label){
dat_for_reg <- data |>
filter(type == es_type) |>
mutate(estimate = round_half_up(estimate, 2)) |>
count(subfield, estimate)
# fit quantile regressions at multiple quantiles
#fit_05 <- rq(estimate ~ 0 + subfield, tau = 0.05, weights = n, method = "fn", data = dat_for_reg)
fit_10 <- rq(estimate ~ 0 + subfield, tau = 0.10, weights = n, method = "fn", data = dat_for_reg)
fit_25 <- rq(estimate ~ 0 + subfield, tau = 0.25, weights = n, method = "fn", data = dat_for_reg)
fit_50 <- rq(estimate ~ 0 + subfield, tau = 0.50, weights = n, method = "fn", data = dat_for_reg)
fit_75 <- rq(estimate ~ 0 + subfield, tau = 0.75, weights = n, method = "fn", data = dat_for_reg)
fit_90 <- rq(estimate ~ 0 + subfield, tau = 0.90, weights = n, method = "fn", data = dat_for_reg)
fit_95 <- rq(estimate ~ 0 + subfield, tau = 0.95, weights = n, method = "fn", data = dat_for_reg)
fit_99 <- rq(estimate ~ 0 + subfield, tau = 0.99, weights = n, method = "fn", data = dat_for_reg)
# wrangle and plot
res <- bind_rows(
# summary(fit_05, se = "nid")$coefficients |>
# as.data.frame() |>
# rownames_to_column(var = "subfield") |>
# mutate(percentile = 5),
summary(fit_10, se = "nid")$coefficients |>
as.data.frame() |>
rownames_to_column(var = "subfield") |>
mutate(percentile = 10),
summary(fit_25, se = "nid")$coefficients |>
as.data.frame() |>
rownames_to_column(var = "subfield") |>
mutate(percentile = 25),
summary(fit_50, se = "nid")$coefficients |>
as.data.frame() |>
rownames_to_column(var = "subfield") |>
mutate(percentile = 50),
summary(fit_75, se = "nid")$coefficients |>
as.data.frame() |>
rownames_to_column(var = "subfield") |>
mutate(percentile = 75),
summary(fit_90, se = "nid")$coefficients |>
as.data.frame() |>
rownames_to_column(var = "subfield") |>
mutate(percentile = 90),
summary(fit_95, se = "nid")$coefficients |>
as.data.frame() |>
rownames_to_column(var = "subfield") |>
mutate(percentile = 95),
summary(fit_99, se = "nid")$coefficients |>
as.data.frame() |>
rownames_to_column(var = "subfield") |>
mutate(percentile = 99)
) |>
mutate(subfield = str_remove(subfield, "subfield"),
percentile = as.factor(percentile)) |>
rename(estimate = Value,
se = `Std. Error`) |>
mutate(subfield = fct_relevel(subfield,
"Social Psychology",
"Applied Psychology",
"Clinical Psychology",
"Developmental and Educational Psychology",
"Experimental and Cognitive Psychology",
"General Psychology"))
# ggplot(res, aes(estimate, subfield, color = percentile)) +
# geom_linerangeh(aes(xmin = estimate - se*1.96, xmax = estimate + se*1.96),
# position = position_dodge(width = 0.75)) +
# geom_point(position = position_dodge(width = 0.75)) +
# theme_linedraw() +
# ylab("") +
# scale_x_continuous(name = label, breaks = scales::breaks_pretty(n = 8)) +
# guides(color = guide_legend(reverse = TRUE))
plot <- res |>
filter(percentile %in% c(10, 25, 50, 75, 90, 95)) |>
ggplot(aes(estimate, percentile, color = subfield)) +
geom_linerangeh(aes(xmin = estimate - se*1.96, xmax = estimate + se*1.96),
position = position_dodge(width = 0.75)) +
geom_point(position = position_dodge(width = 0.75)) +
theme_linedraw() +
scale_x_continuous(name = label, breaks = scales::breaks_pretty(n = 8)) +
ylab("Percentile") +
guides(color = guide_legend(reverse = TRUE))
return(list(res = res,
plot = plot))
}
quantile_regression_and_plot(data_effectsizes_possible, "d_native", "Cohen's d")## $res
## subfield estimate se t value
## 1 Applied Psychology 0.12 0.02025505 5.9244477
## 2 Clinical Psychology 0.13 0.03296069 3.9440926
## 3 Developmental and Educational Psychology 0.14 0.02745149 5.0999045
## 4 Experimental and Cognitive Psychology 0.16 0.03445154 4.6442044
## 5 General Psychology 0.14 0.02730476 5.1273117
## 6 Social Psychology 0.10 0.02036129 4.9112800
## 7 Applied Psychology 0.26 0.03382380 7.6868952
## 8 Clinical Psychology 0.30 0.03494658 8.5845307
## 9 Developmental and Educational Psychology 0.30 0.03735200 8.0316974
## 10 Experimental and Cognitive Psychology 0.35 0.04154976 8.4236348
## 11 General Psychology 0.29 0.03715235 7.8056982
## 12 Social Psychology 0.23 0.03022329 7.6100243
## 13 Applied Psychology 0.49 0.04207754 11.6451674
## 14 Clinical Psychology 0.56 0.05589554 10.0186892
## 15 Developmental and Educational Psychology 0.57 0.05159615 11.0473369
## 16 Experimental and Cognitive Psychology 0.66 0.05623287 11.7369071
## 17 General Psychology 0.56 0.04861928 11.5180654
## 18 Social Psychology 0.45 0.04229824 10.6387412
## 19 Applied Psychology 0.86 0.08268040 10.4014979
## 20 Clinical Psychology 0.99 0.08736645 11.3315804
## 21 Developmental and Educational Psychology 1.02 0.08149528 12.5160618
## 22 Experimental and Cognitive Psychology 1.15 0.08629565 13.3262793
## 23 General Psychology 1.03 0.10470207 9.8374371
## 24 Social Psychology 0.87 0.10955944 7.9408948
## 25 Applied Psychology 1.34 0.14684914 9.1250111
## 26 Clinical Psychology 1.47 0.16009477 9.1820616
## 27 Developmental and Educational Psychology 1.57 0.18300996 8.5787679
## 28 Experimental and Cognitive Psychology 1.83 0.22824147 8.0178244
## 29 General Psychology 1.81 0.25939520 6.9777699
## 30 Social Psychology 1.45 0.18325162 7.9126175
## 31 Applied Psychology 1.78 0.32984245 5.3965157
## 32 Clinical Psychology 2.05 0.37920809 5.4060028
## 33 Developmental and Educational Psychology 2.20 0.44974185 4.8916951
## 34 Experimental and Cognitive Psychology 2.56 0.42841871 5.9754627
## 35 General Psychology 2.97 0.91623412 3.2415296
## 36 Social Psychology 1.97 0.34362966 5.7329160
## 37 Applied Psychology 4.75 9.13642929 0.5198968
## 38 Clinical Psychology 6.28 6.50262859 0.9657633
## 39 Developmental and Educational Psychology 7.30 7.02872614 1.0385950
## 40 Experimental and Cognitive Psychology 6.93 6.46797322 1.0714330
## 41 General Psychology 13.06 22.78075401 0.5732909
## 42 Social Psychology 4.01 2.26281086 1.7721322
## Pr(>|t|) percentile
## 1 0.000000003630188594173 10
## 2 0.000082623657119418326 10
## 3 0.000000369052054915997 10
## 4 0.000003616414766760556 10
## 5 0.000000319735750542449 10
## 6 0.000000971794473336729 10
## 7 0.000000000000022648550 25
## 8 0.000000000000000000000 25
## 9 0.000000000000001554312 25
## 10 0.000000000000000000000 25
## 11 0.000000000000009103829 25
## 12 0.000000000000040412118 25
## 13 0.000000000000000000000 50
## 14 0.000000000000000000000 50
## 15 0.000000000000000000000 50
## 16 0.000000000000000000000 50
## 17 0.000000000000000000000 50
## 18 0.000000000000000000000 50
## 19 0.000000000000000000000 75
## 20 0.000000000000000000000 75
## 21 0.000000000000000000000 75
## 22 0.000000000000000000000 75
## 23 0.000000000000000000000 75
## 24 0.000000000000003108624 75
## 25 0.000000000000000000000 90
## 26 0.000000000000000000000 90
## 27 0.000000000000000000000 90
## 28 0.000000000000001776357 90
## 29 0.000000000003959499395 90
## 30 0.000000000000003996803 90
## 31 0.000000075287193368467 95
## 32 0.000000071458190920026 95
## 33 0.000001072524674849973 95
## 34 0.000000002671314724978 95
## 35 0.001206757165709504420 95
## 36 0.000000011237439867529 95
## 37 0.603188125879168701715 99
## 38 0.334269370021937461956 99
## 39 0.299107878668044113724 99
## 40 0.284092945128494367424 99
## 41 0.566506629588456878110 99
## 42 0.076511752624263795752 99
##
## $plot
## $res
## subfield estimate se t value
## 1 Applied Psychology 0.15 0.02714137 5.526618
## 2 Clinical Psychology 0.16 0.03155475 5.070552
## 3 Developmental and Educational Psychology 0.18 0.03351159 5.371276
## 4 Experimental and Cognitive Psychology 0.20 0.03642663 5.490489
## 5 General Psychology 0.15 0.03046962 4.922936
## 6 Social Psychology 0.14 0.02791879 5.014544
## 7 Applied Psychology 0.31 0.03021548 10.259641
## 8 Clinical Psychology 0.33 0.03278682 10.065021
## 9 Developmental and Educational Psychology 0.40 0.04145246 9.649609
## 10 Experimental and Cognitive Psychology 0.47 0.04634562 10.141196
## 11 General Psychology 0.33 0.03618208 9.120536
## 12 Social Psychology 0.30 0.03626111 8.273326
## 13 Applied Psychology 0.55 0.04832835 11.380484
## 14 Clinical Psychology 0.63 0.05244109 12.013481
## 15 Developmental and Educational Psychology 0.80 0.06298633 12.701168
## 16 Experimental and Cognitive Psychology 0.92 0.06486183 14.183998
## 17 General Psychology 0.65 0.05787167 11.231748
## 18 Social Psychology 0.53 0.04142719 12.793530
## 19 Applied Psychology 1.02 0.11079011 9.206598
## 20 Clinical Psychology 1.16 0.11709577 9.906421
## 21 Developmental and Educational Psychology 1.46 0.12021213 12.145197
## 22 Experimental and Cognitive Psychology 1.68 0.13131259 12.793899
## 23 General Psychology 1.26 0.11759177 10.715035
## 24 Social Psychology 0.94 0.10360317 9.073081
## 25 Applied Psychology 1.86 0.26462838 7.028725
## 26 Clinical Psychology 2.06 0.23350514 8.822075
## 27 Developmental and Educational Psychology 2.55 0.27367800 9.317519
## 28 Experimental and Cognitive Psychology 2.91 0.28100544 10.355672
## 29 General Psychology 2.28 0.28641447 7.960492
## 30 Social Psychology 1.73 0.25824879 6.698967
## 31 Applied Psychology 2.68 0.50627074 5.293610
## 32 Clinical Psychology 2.86 0.44845249 6.377487
## 33 Developmental and Educational Psychology 3.60 0.50272196 7.161016
## 34 Experimental and Cognitive Psychology 4.08 0.54850606 7.438386
## 35 General Psychology 3.24 0.49076828 6.601894
## 36 Social Psychology 2.51 0.44637595 5.623063
## 37 Applied Psychology 5.49 2.45074491 2.240135
## 38 Clinical Psychology 5.73 2.46505521 2.324492
## 39 Developmental and Educational Psychology 8.38 3.57819406 2.341964
## 40 Experimental and Cognitive Psychology 9.55 3.61446409 2.642162
## 41 General Psychology 7.05 2.67906596 2.631514
## 42 Social Psychology 5.09 1.82511641 2.788863
## Pr(>|t|) percentile
## 1 0.000000033773718044472 10
## 2 0.000000406436506894536 10
## 3 0.000000080595131013439 10
## 4 0.000000041431314601326 10
## 5 0.000000871277638925250 10
## 6 0.000000544135407398727 10
## 7 0.000000000000000000000 25
## 8 0.000000000000000000000 25
## 9 0.000000000000000000000 25
## 10 0.000000000000000000000 25
## 11 0.000000000000000000000 25
## 12 0.000000000000000000000 25
## 13 0.000000000000000000000 50
## 14 0.000000000000000000000 50
## 15 0.000000000000000000000 50
## 16 0.000000000000000000000 50
## 17 0.000000000000000000000 50
## 18 0.000000000000000000000 50
## 19 0.000000000000000000000 75
## 20 0.000000000000000000000 75
## 21 0.000000000000000000000 75
## 22 0.000000000000000000000 75
## 23 0.000000000000000000000 75
## 24 0.000000000000000000000 75
## 25 0.000000000002272848576 90
## 26 0.000000000000000000000 90
## 27 0.000000000000000000000 90
## 28 0.000000000000000000000 90
## 29 0.000000000000001998401 90
## 30 0.000000000022552182344 90
## 31 0.000000123421508346766 95
## 32 0.000000000191005211647 95
## 33 0.000000000000879074591 95
## 34 0.000000000000113686838 95
## 35 0.000000000043442138775 95
## 36 0.000000019453775923495 95
## 37 0.025112143860383850935 99
## 38 0.020126484801269217684 99
## 39 0.019209352361759357564 99
## 40 0.008255452747476965669 99
## 41 0.008518424287752734969 99
## 42 0.005303053180681915890 99
##
## $plot
## $res
## subfield estimate se t value
## 1 Applied Psychology 0.08 0.02479629 3.226289
## 2 Clinical Psychology 0.08 0.02301171 3.476491
## 3 Developmental and Educational Psychology 0.09 0.02711581 3.319096
## 4 Experimental and Cognitive Psychology 0.10 0.02522774 3.963891
## 5 General Psychology 0.07 0.02223441 3.148273
## 6 Social Psychology 0.07 0.01702150 4.112446
## 7 Applied Psychology 0.16 0.01840320 8.694142
## 8 Clinical Psychology 0.17 0.02277163 7.465429
## 9 Developmental and Educational Psychology 0.20 0.03018703 6.625362
## 10 Experimental and Cognitive Psychology 0.24 0.03276596 7.324676
## 11 General Psychology 0.17 0.02750305 6.181133
## 12 Social Psychology 0.15 0.02526587 5.936862
## 13 Applied Psychology 0.28 0.03434096 8.153528
## 14 Clinical Psychology 0.32 0.03642223 8.785843
## 15 Developmental and Educational Psychology 0.41 0.04425927 9.263596
## 16 Experimental and Cognitive Psychology 0.47 0.04866431 9.658002
## 17 General Psychology 0.33 0.03959094 8.335241
## 18 Social Psychology 0.27 0.03030875 8.908320
## 19 Applied Psychology 0.52 0.07974718 6.520607
## 20 Clinical Psychology 0.59 0.07970071 7.402695
## 21 Developmental and Educational Psychology 0.75 0.08552992 8.768861
## 22 Experimental and Cognitive Psychology 0.86 0.09361702 9.186364
## 23 General Psychology 0.64 0.08800976 7.271921
## 24 Social Psychology 0.48 0.07579761 6.332653
## 25 Applied Psychology 0.94 0.19010492 4.944638
## 26 Clinical Psychology 1.05 0.16875255 6.222128
## 27 Developmental and Educational Psychology 1.31 0.20336861 6.441506
## 28 Experimental and Cognitive Psychology 1.50 0.20812884 7.207074
## 29 General Psychology 1.16 0.20010970 5.796820
## 30 Social Psychology 0.87 0.18723649 4.646530
## 31 Applied Psychology 1.37 0.36219104 3.782534
## 32 Clinical Psychology 1.46 0.32703981 4.464288
## 33 Developmental and Educational Psychology 1.84 0.36930990 4.982266
## 34 Experimental and Cognitive Psychology 2.10 0.41081990 5.111729
## 35 General Psychology 1.66 0.35988082 4.612638
## 36 Social Psychology 1.27 0.33262295 3.818137
## 37 Applied Psychology 2.84 1.81010735 1.568968
## 38 Clinical Psychology 2.95 1.78012304 1.657189
## 39 Developmental and Educational Psychology 4.36 2.59246281 1.681798
## 40 Experimental and Cognitive Psychology 4.95 2.82424846 1.752679
## 41 General Psychology 3.60 2.02280896 1.779703
## 42 Social Psychology 2.59 1.43264653 1.807843
## Pr(>|t|) percentile
## 1 0.0012625828731929189530 10
## 2 0.0005125755661539166397 10
## 3 0.0009099039976663281237 10
## 4 0.0000748222195823267100 10
## 5 0.0016525690771183043637 10
## 6 0.0000398112125403748962 10
## 7 0.0000000000000000000000 25
## 8 0.0000000000000981437154 25
## 9 0.0000000000384923204422 25
## 10 0.0000000000002797762022 25
## 11 0.0000000006897606930067 25
## 12 0.0000000031121683008450 25
## 13 0.0000000000000004440892 50
## 14 0.0000000000000000000000 50
## 15 0.0000000000000000000000 50
## 16 0.0000000000000000000000 50
## 17 0.0000000000000000000000 50
## 18 0.0000000000000000000000 50
## 19 0.0000000000773183739256 75
## 20 0.0000000000001569855357 75
## 21 0.0000000000000000000000 75
## 22 0.0000000000000000000000 75
## 23 0.0000000000004125588760 75
## 24 0.0000000002632591922236 75
## 25 0.0000007891725244402181 90
## 26 0.0000000005326672436468 90
## 27 0.0000000001300173302354 90
## 28 0.0000000000006619149673 90
## 29 0.0000000071957093616959 90
## 30 0.0000034670490496324646 90
## 31 0.0001571508889752770699 95
## 32 0.0000082198464756988443 95
## 33 0.0000006507746248551882 95
## 34 0.0000003317688088699811 95
## 35 0.0000040805414616151836 95
## 36 0.0001361783425619655929 95
## 37 0.1167221395142845619119 99
## 38 0.0975474155798852216037 99
## 39 0.0926737277113329760425 99
## 40 0.0797217603012183584354 99
## 41 0.0751884968393823349686 99
## 42 0.0706942333431048730574 99
##
## $plot
## $res
## subfield estimate se t value
## 1 Applied Psychology 0.11 0.02641905 4.163662
## 2 Clinical Psychology 0.11 0.03033243 3.626482
## 3 Developmental and Educational Psychology 0.11 0.02575230 4.271463
## 4 Experimental and Cognitive Psychology 0.09 0.02869779 3.136130
## 5 General Psychology 0.11 0.02606077 4.220904
## 6 Social Psychology 0.09 0.02660593 3.382705
## 7 Applied Psychology 0.19 0.02287549 8.305832
## 8 Clinical Psychology 0.21 0.02894397 7.255396
## 9 Developmental and Educational Psychology 0.20 0.02866908 6.976156
## 10 Experimental and Cognitive Psychology 0.20 0.03042684 6.573143
## 11 General Psychology 0.19 0.02578887 7.367518
## 12 Social Psychology 0.17 0.02632835 6.456918
## 13 Applied Psychology 0.31 0.03397490 9.124382
## 14 Clinical Psychology 0.33 0.03086308 10.692386
## 15 Developmental and Educational Psychology 0.33 0.03311746 9.964532
## 16 Experimental and Cognitive Psychology 0.35 0.03406649 10.274026
## 17 General Psychology 0.32 0.03351415 9.548206
## 18 Social Psychology 0.30 0.03421523 8.768026
## 19 Applied Psychology 0.48 0.04901892 9.792138
## 20 Clinical Psychology 0.50 0.04502396 11.105198
## 21 Developmental and Educational Psychology 0.50 0.04459635 11.211680
## 22 Experimental and Cognitive Psychology 0.53 0.05172564 10.246370
## 23 General Psychology 0.49 0.04513053 10.857395
## 24 Social Psychology 0.46 0.04607461 9.983806
## 25 Applied Psychology 0.66 0.06164445 10.706560
## 26 Clinical Psychology 0.67 0.06066486 11.044285
## 27 Developmental and Educational Psychology 0.67 0.06438075 10.406837
## 28 Experimental and Cognitive Psychology 0.74 0.06559495 11.281356
## 29 General Psychology 0.66 0.06080845 10.853754
## 30 Social Psychology 0.63 0.06208050 10.148114
## 31 Applied Psychology 0.77 0.07300699 10.546935
## 32 Clinical Psychology 0.77 0.06671492 11.541646
## 33 Developmental and Educational Psychology 0.79 0.07624766 10.360974
## 34 Experimental and Cognitive Psychology 0.85 0.06311960 13.466498
## 35 General Psychology 0.76 0.07201690 10.553079
## 36 Social Psychology 0.74 0.08402676 8.806718
## 37 Applied Psychology 0.93 0.07915761 11.748713
## 38 Clinical Psychology 0.92 0.07789972 11.810056
## 39 Developmental and Educational Psychology 0.93 0.06313080 14.731319
## 40 Experimental and Cognitive Psychology 0.97 0.04020089 24.128817
## 41 General Psychology 0.93 0.07808410 11.910235
## 42 Social Psychology 0.92 0.08696459 10.579019
## Pr(>|t|) percentile
## 1 0.0000359104096989693033 10
## 2 0.0003117420167653150997 10
## 3 0.0000225768237307466535 10
## 4 0.0017957120880454091605 10
## 5 0.0000281022088639559797 10
## 6 0.0007644916583455785286 10
## 7 0.0000000000000006661338 25
## 8 0.0000000000012434497876 25
## 9 0.0000000000080442319472 25
## 10 0.0000000001071256416907 25
## 11 0.0000000000005777600620 25
## 12 0.0000000002207625193762 25
## 13 0.0000000000000000000000 50
## 14 0.0000000000000000000000 50
## 15 0.0000000000000000000000 50
## 16 0.0000000000000000000000 50
## 17 0.0000000000000000000000 50
## 18 0.0000000000000000000000 50
## 19 0.0000000000000000000000 75
## 20 0.0000000000000000000000 75
## 21 0.0000000000000000000000 75
## 22 0.0000000000000000000000 75
## 23 0.0000000000000000000000 75
## 24 0.0000000000000000000000 75
## 25 0.0000000000000000000000 90
## 26 0.0000000000000000000000 90
## 27 0.0000000000000000000000 90
## 28 0.0000000000000000000000 90
## 29 0.0000000000000000000000 90
## 30 0.0000000000000000000000 90
## 31 0.0000000000000000000000 95
## 32 0.0000000000000000000000 95
## 33 0.0000000000000000000000 95
## 34 0.0000000000000000000000 95
## 35 0.0000000000000000000000 95
## 36 0.0000000000000000000000 95
## 37 0.0000000000000000000000 99
## 38 0.0000000000000000000000 99
## 39 0.0000000000000000000000 99
## 40 0.0000000000000000000000 99
## 41 0.0000000000000000000000 99
## 42 0.0000000000000000000000 99
##
## $plot
quantile_regression_and_plot(data_effectsizes_possible, "peta2", "peta2") # throws error, needs fixing ## $res
## subfield estimate se t value
## 1 Applied Psychology 0.01 0.007497950 1.3336979
## 2 Clinical Psychology 0.01 0.012560465 0.7961488
## 3 Developmental and Educational Psychology 0.01 0.005848786 1.7097565
## 4 Experimental and Cognitive Psychology 0.02 0.010509878 1.9029716
## 5 General Psychology 0.01 0.006550580 1.5265824
## 6 Social Psychology 0.01 0.007760665 1.2885494
## 7 Applied Psychology 0.02 0.016694379 1.1980080
## 8 Clinical Psychology 0.04 0.018644134 2.1454469
## 9 Developmental and Educational Psychology 0.04 0.017363298 2.3037098
## 10 Experimental and Cognitive Psychology 0.06 0.019500430 3.0768553
## 11 General Psychology 0.03 0.014585035 2.0569028
## 12 Social Psychology 0.02 0.011519548 1.7361793
## 13 Applied Psychology 0.07 0.026701932 2.6215331
## 14 Clinical Psychology 0.11 0.033548040 3.2788800
## 15 Developmental and Educational Psychology 0.13 0.038186279 3.4043642
## 16 Experimental and Cognitive Psychology 0.16 0.040547114 3.9460268
## 17 General Psychology 0.10 0.038880214 2.5720023
## 18 Social Psychology 0.07 0.027637522 2.5327886
## 19 Applied Psychology 0.20 0.083471895 2.3960161
## 20 Clinical Psychology 0.26 0.074576536 3.4863513
## 21 Developmental and Educational Psychology 0.31 0.082475666 3.7586844
## 22 Experimental and Cognitive Psychology 0.39 0.085801890 4.5453544
## 23 General Psychology 0.27 0.087510211 3.0853542
## 24 Social Psychology 0.18 0.069117286 2.6042689
## 25 Applied Psychology 0.43 0.172452844 2.4934352
## 26 Clinical Psychology 0.50 0.125604654 3.9807442
## 27 Developmental and Educational Psychology 0.57 0.122824509 4.6407676
## 28 Experimental and Cognitive Psychology 0.65 0.105098782 6.1846578
## 29 General Psychology 0.53 0.150663334 3.5177769
## 30 Social Psychology 0.38 0.162973967 2.3316607
## 31 Applied Psychology 0.61 0.213120012 2.8622371
## 32 Clinical Psychology 0.65 0.156194413 4.1614805
## 33 Developmental and Educational Psychology 0.72 0.131610279 5.4706973
## 34 Experimental and Cognitive Psychology 0.79 0.099576792 7.9335755
## 35 General Psychology 0.70 0.162918160 4.2966358
## 36 Social Psychology 0.55 0.211396232 2.6017493
## 37 Applied Psychology 0.87 0.245079251 3.5498721
## 38 Clinical Psychology 0.89 0.174485234 5.1007182
## 39 Developmental and Educational Psychology 0.91 0.124263367 7.3231558
## 40 Experimental and Cognitive Psychology 0.93 0.077293721 12.0320252
## 41 General Psychology 0.91 0.139173681 6.5385926
## 42 Social Psychology 0.81 0.304399690 2.6609751
## Pr(>|t|) percentile
## 1 0.1828089322840733555 10
## 2 0.4262604075108600288 10
## 3 0.0878279398725378968 10
## 4 0.0575226138546194310 10
## 5 0.1273918248017316124 10
## 6 0.1980514334455880654 10
## 7 0.2313869102114560761 25
## 8 0.0323177932497431541 25
## 9 0.0215798352834997154 25
## 10 0.0021873064075570436 25
## 11 0.0401273351472526407 25
## 12 0.0830454571720915524 25
## 13 0.0089755340326722610 50
## 14 0.0011024925585678691 50
## 15 0.0007074255792196560 50
## 16 0.0000888621628361008 50
## 17 0.0103507091870844725 50
## 18 0.0115696259350213104 50
## 19 0.0168796279877485578 75
## 20 0.0005254344775638131 75
## 21 0.0001875042996382081 75
## 22 0.0000066351957048738 75
## 23 0.0021267110194758132 75
## 24 0.0094350832759300118 75
## 25 0.0129196375776954309 90
## 26 0.0000771173345492926 90
## 27 0.0000042655972998240 90
## 28 0.0000000011516780862 90
## 29 0.0004680881981227181 90
## 30 0.0200486442094831574 90
## 31 0.0043533820028096581 95
## 32 0.0000362455001583495 95
## 33 0.0000000658953569488 95
## 34 0.0000000000000104361 95
## 35 0.0000202285214205844 95
## 36 0.0095038831758742504 95
## 37 0.0004156013608507259 99
## 38 0.0000004544932576955 99
## 39 0.0000000000007833734 99
## 40 0.0000000000000000000 99
## 41 0.0000000001329625299 99
## 42 0.0079999960594459019 99
##
## $plot
## $res
## subfield estimate se t value
## 1 Applied Psychology 0.0100000 0.006312746 1.584097
## 2 Clinical Psychology 0.0100000 0.005802359 1.723437
## 3 Developmental and Educational Psychology 0.0100000 0.005772551 1.732336
## 4 Experimental and Cognitive Psychology 0.0200000 0.014404906 1.388416
## 5 General Psychology 0.0100000 0.005976178 1.673310
## 6 Social Psychology 0.0100000 0.005959635 1.677955
## 7 Applied Psychology 0.0300000 0.014055493 2.134397
## 8 Clinical Psychology 0.0500000 0.021531841 2.322142
## 9 Developmental and Educational Psychology 0.0500000 0.017136981 2.917667
## 10 Experimental and Cognitive Psychology 0.0700000 0.021381930 3.273792
## 11 General Psychology 0.0400000 0.017741486 2.254603
## 12 Social Psychology 0.0400000 0.013269282 3.014481
## 13 Applied Psychology 0.1100000 0.037468584 2.935793
## 14 Clinical Psychology 0.1300000 0.037883166 3.431603
## 15 Developmental and Educational Psychology 0.1200000 0.034262319 3.502390
## 16 Experimental and Cognitive Psychology 0.2000000 0.039899378 5.012609
## 17 General Psychology 0.1200000 0.035470918 3.383053
## 18 Social Psychology 0.1200000 0.038910004 3.084040
## 19 Applied Psychology 0.2500000 0.065592306 3.811423
## 20 Clinical Psychology 0.3027526 0.081820996 3.700183
## 21 Developmental and Educational Psychology 0.2700000 0.059979435 4.501543
## 22 Experimental and Cognitive Psychology 0.3900000 0.067709448 5.759905
## 23 General Psychology 0.2600000 0.062095202 4.187119
## 24 Social Psychology 0.2700000 0.053077128 5.086937
## 25 Applied Psychology 0.4800000 0.132567686 3.620792
## 26 Clinical Psychology 0.5100000 0.110244841 4.626067
## 27 Developmental and Educational Psychology 0.4600000 0.103905934 4.427081
## 28 Experimental and Cognitive Psychology 0.6200000 0.086429438 7.173482
## 29 General Psychology 0.4500000 0.113547381 3.963103
## 30 Social Psychology 0.4300000 0.083434894 5.153719
## 31 Applied Psychology 0.6400000 0.194384749 3.292439
## 32 Clinical Psychology 0.6400000 0.103078098 6.208884
## 33 Developmental and Educational Psychology 0.5900000 0.123058273 4.794477
## 34 Experimental and Cognitive Psychology 0.7500000 0.125107207 5.994858
## 35 General Psychology 0.5900000 0.162787788 3.624350
## 36 Social Psychology 0.5200000 0.134104624 3.877570
## 37 Applied Psychology 0.9600000 0.134120683 7.157733
## 38 Clinical Psychology 0.8100000 0.246553996 3.285284
## 39 Developmental and Educational Psychology 0.8600000 0.235853256 3.646335
## 40 Experimental and Cognitive Psychology 0.9100000 0.078473532 11.596267
## 41 General Psychology 0.9200000 0.205105279 4.485501
## 42 Social Psychology 0.7300000 0.214277411 3.406799
## Pr(>|t|) percentile
## 1 0.113776158591470944 10
## 2 0.085400572552026599 10
## 3 0.083803257669567532 10
## 4 0.165601491916319121 10
## 5 0.094864177740357603 10
## 6 0.093953254050930024 10
## 7 0.033274655847866264 25
## 8 0.020608331613446218 25
## 9 0.003678274347020949 25
## 10 0.001131332200966062 25
## 11 0.024571159998310943 25
## 12 0.002699117627202208 25
## 13 0.003473351315522955 50
## 14 0.000647509540810276 50
## 15 0.000500573873442711 50
## 16 0.000000736192478756 50
## 17 0.000770568109081715 50
## 18 0.002149998777364992 50
## 19 0.000154612213986383 75
## 20 0.000238268180870804 75
## 21 0.000008322818373152 75
## 22 0.000000014373861656 75
## 23 0.000033149912034958 75
## 24 0.000000508032120816 75
## 25 0.000322294666398770 90
## 26 0.000004703788670790 90
## 27 0.000011633948709733 90
## 28 0.000000000002513767 90
## 29 0.000084260132959368 90
## 30 0.000000362616124461 90
## 31 0.001060347086306646 95
## 32 0.000000001088054535 95
## 33 0.000002129239898618 95
## 34 0.000000003798532155 95
## 35 0.000317997614870169 95
## 36 0.000118944747262706 95
## 37 0.000000000002791101 99
## 38 0.001087081101116993 99
## 39 0.000292620874845273 99
## 40 0.000000000000000000 99
## 41 0.000008949113199375 99
## 42 0.000707888231883391 99
##
## $plot
## $res
## subfield estimate se t value
## 1 Applied Psychology 1.060000 0.01657495 63.951923
## 2 Clinical Psychology 1.060000 0.01641569 64.572385
## 3 Developmental and Educational Psychology 1.100000 0.02506432 43.887083
## 4 Experimental and Cognitive Psychology 1.070000 0.02158799 49.564585
## 5 General Psychology 1.080000 0.02133674 50.616924
## 6 Social Psychology 1.080000 0.02060641 52.410883
## 7 Applied Psychology 1.250000 0.04510557 27.712762
## 8 Clinical Psychology 1.230000 0.04467216 27.533926
## 9 Developmental and Educational Psychology 1.300000 0.04464509 29.118543
## 10 Experimental and Cognitive Psychology 1.230000 0.04005519 30.707630
## 11 General Psychology 1.260000 0.04750680 26.522520
## 12 Social Psychology 1.300000 0.04970409 26.154788
## 13 Applied Psychology 1.710000 0.09509944 17.981179
## 14 Clinical Psychology 1.710000 0.09418564 18.155633
## 15 Developmental and Educational Psychology 1.820000 0.08925985 20.389907
## 16 Experimental and Cognitive Psychology 1.790000 0.13774305 12.995211
## 17 General Psychology 1.720000 0.09181528 18.733266
## 18 Social Psychology 1.790000 0.08561488 20.907581
## 19 Applied Psychology 2.610000 0.18862331 13.837102
## 20 Clinical Psychology 2.710000 0.23960523 11.310270
## 21 Developmental and Educational Psychology 2.910000 0.23066630 12.615627
## 22 Experimental and Cognitive Psychology 3.364052 0.41256849 8.153924
## 23 General Psychology 2.700000 0.23357509 11.559452
## 24 Social Psychology 2.800000 0.21028655 13.315165
## 25 Applied Psychology 4.650000 0.71272295 6.524274
## 26 Clinical Psychology 4.980000 0.82625623 6.027186
## 27 Developmental and Educational Psychology 5.560000 0.89730277 6.196348
## 28 Experimental and Cognitive Psychology 7.300000 1.40861676 5.182389
## 29 General Psychology 5.000000 0.85346952 5.858440
## 30 Social Psychology 5.180000 0.74183070 6.982725
## 31 Applied Psychology 7.100000 1.36102102 5.216672
## 32 Clinical Psychology 7.970000 1.80157797 4.423900
## 33 Developmental and Educational Psychology 9.450000 2.68345847 3.521575
## 34 Experimental and Cognitive Psychology 14.790000 5.61838745 2.632428
## 35 General Psychology 8.860000 2.48905629 3.559582
## 36 Social Psychology 8.400000 1.95847398 4.289054
## 37 Applied Psychology 23.830000 18.57373947 1.282994
## 38 Clinical Psychology 24.950000 20.20170639 1.235044
## 39 Developmental and Educational Psychology 39.330000 22.49678895 1.748249
## 40 Experimental and Cognitive Psychology 74.960000 41.27046987 1.816311
## 41 General Psychology 33.330000 32.69138521 1.019535
## 42 Social Psychology 23.490000 14.70010959 1.597947
## Pr(>|t|) percentile
## 1 0.0000000000000000000000 10
## 2 0.0000000000000000000000 10
## 3 0.0000000000000000000000 10
## 4 0.0000000000000000000000 10
## 5 0.0000000000000000000000 10
## 6 0.0000000000000000000000 10
## 7 0.0000000000000000000000 25
## 8 0.0000000000000000000000 25
## 9 0.0000000000000000000000 25
## 10 0.0000000000000000000000 25
## 11 0.0000000000000000000000 25
## 12 0.0000000000000000000000 25
## 13 0.0000000000000000000000 50
## 14 0.0000000000000000000000 50
## 15 0.0000000000000000000000 50
## 16 0.0000000000000000000000 50
## 17 0.0000000000000000000000 50
## 18 0.0000000000000000000000 50
## 19 0.0000000000000000000000 75
## 20 0.0000000000000000000000 75
## 21 0.0000000000000000000000 75
## 22 0.0000000000000004440892 75
## 23 0.0000000000000000000000 75
## 24 0.0000000000000000000000 75
## 25 0.0000000000752327089515 90
## 26 0.0000000017905497085025 90
## 27 0.0000000006251994477680 90
## 28 0.0000002278580848447120 90
## 29 0.0000000049775841226563 90
## 30 0.0000000000032818192608 90
## 31 0.0000001896737353845879 95
## 32 0.0000099036951501929593 95
## 33 0.0004328996293869735723 95
## 34 0.0085043029282827475868 95
## 35 0.0003749683870624131998 95
## 36 0.0000182889808431063727 95
## 37 0.1995548008565468656172 99
## 38 0.2168732884834621010839 99
## 39 0.0804834710748378867606 99
## 40 0.0693838005129014945283 99
## 41 0.3079995559645656300063 99
## 42 0.1101192036667897333757 99
##
## $plot
#quantile_regression_and_plot(data_effectsizes_possible, "chi2", "chi2")
#quantile_regression_and_plot(data_effectsizes_possible, "B", "B")
#quantile_regression_and_plot(data_effectsizes_possible, "stdB", "")
quantile_regression_and_plot(data_effectsizes_possible, "abs_stdB", "Absolute std. B") ## $res
## subfield estimate se t value
## 1 Applied Psychology 0.05 0.02696946 1.8539486
## 2 Clinical Psychology 0.04 0.01795737 2.2274972
## 3 Developmental and Educational Psychology 0.05 0.01885801 2.6513930
## 4 Experimental and Cognitive Psychology 0.04 0.01610431 2.4838068
## 5 General Psychology 0.06 0.01893733 3.1683450
## 6 Social Psychology 0.05 0.01927288 2.5943187
## 7 Applied Psychology 0.13 0.02668809 4.8710862
## 8 Clinical Psychology 0.11 0.02665504 4.1267995
## 9 Developmental and Educational Psychology 0.11 0.02799190 3.9297081
## 10 Experimental and Cognitive Psychology 0.13 0.02390445 5.4383189
## 11 General Psychology 0.12 0.02108223 5.6919976
## 12 Social Psychology 0.11 0.02145579 5.1268221
## 13 Applied Psychology 0.24 0.03735064 6.4255932
## 14 Clinical Psychology 0.23 0.03730438 6.1654963
## 15 Developmental and Educational Psychology 0.22 0.03357887 6.5517392
## 16 Experimental and Cognitive Psychology 0.28 0.03823412 7.3233016
## 17 General Psychology 0.23 0.02810009 8.1850272
## 18 Social Psychology 0.21 0.03431760 6.1193097
## 19 Applied Psychology 0.45 0.06672023 6.7445808
## 20 Clinical Psychology 0.44 0.07996512 5.5023993
## 21 Developmental and Educational Psychology 0.40 0.06997975 5.7159390
## 22 Experimental and Cognitive Psychology 0.63 0.14940279 4.2167887
## 23 General Psychology 0.40 0.05621928 7.1149970
## 24 Social Psychology 0.39 0.06436736 6.0589715
## 25 Applied Psychology 0.83 0.30565393 2.7154894
## 26 Clinical Psychology 1.08 0.54769992 1.9718827
## 27 Developmental and Educational Psychology 0.83 0.34887323 2.3790877
## 28 Experimental and Cognitive Psychology 2.11 0.90184155 2.3396571
## 29 General Psychology 0.64 0.11362399 5.6326132
## 30 Social Psychology 0.73 0.25054749 2.9136193
## 31 Applied Psychology 1.80 1.24568259 1.4449909
## 32 Clinical Psychology 2.59 1.59505098 1.6237726
## 33 Developmental and Educational Psychology 1.91 1.28420460 1.4873019
## 34 Experimental and Cognitive Psychology 5.80 4.74910780 1.2212820
## 35 General Psychology 0.90 0.47098660 1.9108824
## 36 Social Psychology 1.48 0.97007674 1.5256525
## 37 Applied Psychology 15.81 28.85536527 0.5479050
## 38 Clinical Psychology 15.18 27.83647240 0.5453277
## 39 Developmental and Educational Psychology 14.42 30.15718261 0.4781614
## 40 Experimental and Cognitive Psychology 49.46 40.74245504 1.2139671
## 41 General Psychology 7.16 18.91010873 0.3786335
## 42 Social Psychology 9.55 18.80420821 0.5078650
## Pr(>|t|) percentile
## 1 0.0638032445341996101718 10
## 2 0.0259568008917718806572 10
## 3 0.0080404245957799158617 10
## 4 0.0130299732420353642226 10
## 5 0.0015419993085714622794 10
## 6 0.0095045245482150431116 10
## 7 0.0000011428626613962933 25
## 8 0.0000373613414139661870 25
## 9 0.0000861555265028179917 25
## 10 0.0000000562502455725422 25
## 11 0.0000000132456414725368 25
## 12 0.0000003053763144578170 25
## 13 0.0000000001431033069821 50
## 14 0.0000000007557725556495 50
## 15 0.0000000000623761042817 50
## 16 0.0000000000002791100684 50
## 17 0.0000000000000004440892 50
## 18 0.0000000010087828350436 50
## 19 0.0000000000170201630567 75
## 20 0.0000000392639991630972 75
## 21 0.0000000115191736149711 75
## 22 0.0000252025048994575229 75
## 23 0.0000000000012714274078 75
## 24 0.0000000014665453296203 75
## 25 0.0066397105391351463055 90
## 26 0.0486760508661348012538 90
## 27 0.0173915161306288368337 90
## 28 0.0193391882706013262805 90
## 29 0.0000000186847628480535 90
## 30 0.0035879174646997746834 90
## 31 0.1485208511558906430139 95
## 32 0.1044851231820742576417 95
## 33 0.1369958652283056466104 95
## 34 0.2220347618924325416856 95
## 35 0.0560747657441984515003 95
## 36 0.1271573984650182786993 95
## 37 0.5837806858853216152028 99
## 38 0.5855515908649571166222 99
## 39 0.6325555002520606429073 99
## 40 0.2248155570196779251546 99
## 41 0.7049755637107022465671 99
## 42 0.6115695864430894523878 99
##
## $plot
## R version 4.5.0 (2025-04-11)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sequoia 15.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/Zurich
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggstance_0.3.7 quantreg_6.1 SparseM_1.84-2 kableExtra_1.4.0
## [5] knitr_1.50 ggridges_0.5.6 patchwork_1.3.0 rlang_1.1.6
## [9] janitor_2.2.1 scales_1.4.0 lubridate_1.9.4 forcats_1.0.0
## [13] stringr_1.5.1 dplyr_1.1.4 purrr_1.1.0 readr_2.1.5
## [17] tidyr_1.3.1 tibble_3.3.0 ggplot2_3.5.2 tidyverse_2.0.0
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.6 sass_0.4.10 generics_0.1.4 xml2_1.3.8
## [5] lattice_0.22-6 stringi_1.8.7 effectsize_1.0.0 hms_1.1.3
## [9] digest_0.6.37 magrittr_2.0.3 evaluate_1.0.3 grid_4.5.0
## [13] timechange_0.3.0 RColorBrewer_1.1-3 fastmap_1.2.0 Matrix_1.7-3
## [17] jsonlite_2.0.0 survival_3.8-3 viridisLite_0.4.2 textshaping_1.0.1
## [21] jquerylib_0.1.4 cli_3.6.5 splines_4.5.0 withr_3.0.2
## [25] cachem_1.1.0 yaml_2.3.10 datawizard_1.1.0 tools_4.5.0
## [29] MatrixModels_0.5-4 tzdb_0.5.0 bayestestR_0.16.1 vctrs_0.6.5
## [33] R6_2.6.1 lifecycle_1.0.4 snakecase_0.11.1 MASS_7.3-65
## [37] ragg_1.4.0 insight_1.3.1 pkgconfig_2.0.3 pillar_1.11.0
## [41] bslib_0.9.0 gtable_0.3.6 glue_1.8.0 systemfonts_1.2.3
## [45] xfun_0.52 tidyselect_1.2.1 parameters_0.27.0 rstudioapi_0.17.1
## [49] farver_2.1.2 htmltools_0.5.8.1 rmarkdown_2.29 svglite_2.2.1
## [53] compiler_4.5.0